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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

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Setup

pip install lancedb

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Example

agent_with_knowledge.py
import typer
from typing import Optional
from rich.prompt import Prompt

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb
from agno.vectordb.search import SearchType

# LanceDB Vector DB
vector_db = LanceDb(
    table_name="recipes",
    uri="/tmp/lancedb",
    search_type=SearchType.keyword,
)

# Knowledge Base
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

def lancedb_agent(user: str = "user"):
    run_id: Optional[str] = None

    agent = Agent(
        run_id=run_id,
        user_id=user,
        knowledge=knowledge_base,
        show_tool_calls=True,
        debug_mode=True,
    )

    if run_id is None:
        run_id = agent.run_id
        print(f"Started Run: {run_id}\n")
    else:
        print(f"Continuing Run: {run_id}\n")

    while True:
        message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
        if message in ("exit", "bye"):
            break
        agent.print_response(message)

if __name__ == "__main__":
    # Comment out after first run
    knowledge_base.load(recreate=True)

    typer.run(lancedb_agent)

Async Support ⚡

LanceDB also supports asynchronous operations, enabling concurrency and leading to better performance.

async_lance_db.py
# install lancedb - `pip install lancedb`
import asyncio

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb

# Initialize LanceDB
vector_db = LanceDb(
    table_name="recipes",
    uri="tmp/lancedb",  # You can change this path to store data elsewhere
)

# Create knowledge base
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)
agent = Agent(knowledge=knowledge_base, show_tool_calls=True, debug_mode=True)

if __name__ == "__main__":
    # Load knowledge base asynchronously
    asyncio.run(knowledge_base.aload(recreate=False))  # Comment out after first run

    # Create and use the agent asynchronously
    asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))
Use aload() and aprint_response() methods with asyncio.run() for non-blocking operations in high-throughput applications.

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LanceDb Params

ParameterTypeDefaultDescription
uristr-The URI to connect to.
tableLanceTable-The Lance table to use.
table_namestr-The name of the table to use.
connectionDBConnection-The database connection to use.
api_keystr-The API key to use.
embedderEmbedder-The embedder to use.
search_typeSearchTypevectorThe search type to use.
distanceDistancecosineThe distance to use.
nprobesint-The number of probes to use. More Info
rerankerReranker-The reranker to use. More Info
use_tantivybool-Whether to use tantivy.

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Developer Resources